What happens when you dive one hour a day into RAG with absolute intensity? I’m about to find out. Starting today, I’m committing to a personal challenge:Every single day, I will spend at least one hour digging deep into Retrieval-Augmented Generation and I’ll share every step of my progress right here. Why?Because I want to grow. Deeply. Consistently. With purpose.And because RAG is becoming one of the most important building blocks of future AI systems. To make this challenge truly powerful, I need your support and I need everything this community has. 🔥 I genuinely need all of it: – your RAG automations – your RAG-enabled AI agents – your workflows– your best practices – your mistakes and lessons– your resources, tutorials, and websites – every piece of knowledge that exists here – every experience you’ve made To start this challenge in the right way, I need your help with a few key questions: 🔍 Which RAG automations have you already built? 🔍 What RAG-related information, examples, or materials already exist in this community? 🔍 What was absolutely essential for you to truly understand RAG? 🔍 Which websites, videos, or tutorials helped you the most? 🔍 Which RAG systems have you built — and would you be open to sharing them with me? I’m excited to dive deeper every single day and to build real RAG excellence together with all of you. Let’s go. Please put all informations in the comments, it will be helpful

Continuing with the theory — it honestly feels like studying for a new degree.But understanding the fundamentals is essential to make better decisions later. Today I found an excellent video, and the key lesson is this: 👉 The quality of any RAG system depends on 5 core factors.LLM = the master chef. Retrieval = the cook bringing the ingredients.If the ingredients are bad, the final dish will be bad, no matter how good the chef is. Here are the 5 pillars, short and clear: 1️⃣ Chunk SizeChunks must be the right size — too big overloads context, too small loses meaning. 2️⃣ Query ConstructionBetter queries = better retrieval.Multi-Query RAG helps cover synonyms and variations. 3️⃣ Embedding ChoiceDense, sparse, or hybrid — your choice directly impacts search quality. 4️⃣ Retrieval QualityThe most critical point.If retrieval brings irrelevant content, the answer will be bad.Metadata & filters improve relevance dramatically. 5️⃣ Generation LayerGood prompting shapes tone, structure, and quality of the final output. ➡️ Master these 5 basics, and your RAG accuracy improves dramatically. As always, you can find all materials in my notebook:👉 https://notebooklm.google.com/notebook/ea1c87b2-0eda-43f8-a389-ba1f57e758ce

In the past, I often just jumped straight into things without thinking too much about the technical foundation. It worked but only up to a certain point. For my RAG Mastery Challenge, I’ve completely changed my approach:This time, I want to understand the theory from the very beginning, so I don’t run into traps later and so I can progress much faster thanks to a solid knowledge base. In other words:No building the roof before laying the foundation. So today was all about research: – What exactly is RAG? – What does Retrieval Augmented Generation really mean? – Why is it important? What problems does it solve? – How does it fit into modern AI workflows? Many of you already know this deeply for me, the knowledge was only halfway complete. So today I: 📌 watched several YouTube videos 📌 compared fundamental explanations 📌 sketched the core concepts 📌 and created my own NotebookLM learning notebook And because I don’t want to learn just for myself but for all of us I’m sharing the notebook here: 👉 My RAG Learning Notebook (NotebookLM) https://notebooklm.google.com/notebook/ea1c87b2-0eda-43f8-a389-ba1f57e758ce This is where I’m collecting all learning materials that will support me along the way:Videos, explanations, sources, examples, definitions, diagrams. If you have anything to add please let me know!I’ll include everything so we can build a powerful shared RAG learning template. Day 2 complete.The foundation is set — tomorrow we go deeper.

I put together a clean, step-by-step PDF teaching you exactly how to build a powerful Custom GPT from scratch — the same 12-step method I use. Inside the guide you'll learn: - How to define your GPT’s purpose - How to write a strong Master Prompt - How to write a System Prompt that locks in tone + behavior - How to upload knowledge files - How to turn on capabilities - How to troubleshoot common errors - How to test your GPT like a real consultant - How to use ChatGPT Study Mode to guide you through each step This is the perfect reference to keep open while building your own GPT. Download it, save it, and use it as your creation blueprint. #ChatGPT

In this video, I show you exactly why AI crawlers cannot read Lovable sites and what you need to do instead if you want to rank in ChatGPT, Perplexity, and Google AI. In this video, you'll learn: → Why Lovable sites are invisible to AI crawlers → What AI systems actually see when they visit your site → The 0.3 second rule that determines if you get ranked → How to check if your site has this problem → What I am doing instead to get ranked by LLMs Here's my youtube channel, would love a subscribe, like and a share it would mean the world!! Thanks Crew https://nicolejolie.com/ai-business-snapshot